Systems and methods for multi-channel messaging and communication

1. A method for providing automated conversation across multiple communication channels associated with a user, the method comprising:

selecting a first communication channel from the multiple communication channels;

receiving, via the first communication channel, a first user input as part of a conversation with a chatbot, the chatbot including a communication data structure including a plurality of communication paths, and a plurality of communication identities,

wherein each of the plurality of communication paths comprises a plurality of units, and

wherein at least one of the plurality of units is coded with instructions to navigate the conversation among different communication paths;

generating a communication identity based on the first user input,

wherein the communication identity includes a plurality of identity elements;

selecting a communication path based on the first user input, wherein selecting the communication path includes:

comparing the generated communication identity with the plurality of communication identities in the communication data structure to determine a match;

when there is no match between the generated communication identity with the plurality of communication identities in the communication data structure,

selecting, via a selection algorithm, a unit within the plurality of units,

wherein, selecting, via a selection algorithm, a unit within the plurality of units includes:

determining one or more communication identities stored within the communication data structure that may approximately match the generated communication identity,

providing the user with one or more feedbacks associated with one or more approximately matched communication identities,

receiving, from the user, a selected feedback from the one or more feedbacks associated with one or more approximately matched communication identities, and

selecting the unit associated with the selected communication identities,

wherein the selection algorithm is a machine learning algorithm, the machine learning algorithm including a computer-implemented method of selecting a unit within the plurality of units including:

receiving an input dataset comprising a user's historical communication data and/or other users' historical communication data;

determining a user's expected feedback; and

producing an output dataset comprising a unit selection associated with the user's expected feedback;

selecting a unit associated with the matched communication identity within the plurality of identities; and

generating a first feedback in response to the first user input according to the selected communication path.

2. The method of claim 1 , wherein the communication identity is generated by the method of updating the plurality of identity elements via a unit within the plurality of units and one or more user inputs.

3. The method of claim 1 , wherein the plurality of identity elements includes one or more of communication content, user interest, user gender, user occupation, user education, user nationality, user race, user hobbies, user personality, or user demographic.

4. The method of claim 1 , wherein the communication identity is generated via a machine learning algorithm, the machine learning algorithm including a computer-implemented method including:

receiving an input dataset comprising one or more identity elements, the identity elements including one or more of communication content, user interest, user gender, user occupation, user education, user nationality, user race, user hobbies, user personality, user demographic, or user input;

predict a communication identity based on the input dataset; and

producing an output dataset comprising a communication identity.

5. The method of claim 4 , wherein the input dataset includes one or more identity elements based on existing user data.

6. The method of claim 5 , wherein the existing user data includes users' historical voice call logs or chat messages.

7. The method of claim 1 , wherein the communication data structure is constructed via a machine learning algorithm, the machine learning algorithm including a computer-implemented method including:

receiving a training dataset comprising one or more of a group of questions and responses, variations of a group of questions and answers, information specific to an industry, a group of identity elements, a group of communication identities, and/or a communication data structure associated with another chatbot;

storing the training dataset in a communication database; and

producing the communication data structure based on the training dataset.

8. A system for providing automated conversation across multiple communication channels associated with a user, the system comprising:

one or more computer processors; and

a memory having stored therein machine executable instructions, that when executed by the one or more processors, cause the system to:

select a first communication channel from the multiple communication channels;

receive, via the first communication channel, a first user input as part of a conversation with a chatbot, the chatbot including a communication data structure including a plurality of communication paths, and a plurality of communication identities,

wherein each of the plurality of communication paths comprises a plurality of units, and

wherein at least one of the plurality of units is coded with instructions to navigate the conversation among different communication paths;

generating a communication identity based on the first user input,

wherein the communication identity includes a plurality of identity elements;

select a communication path based on the first user input, wherein selecting the communication path includes:

comparing the generated communication identity with the plurality of communication identities in the communication data structure to determine a match;

when there is no match between the generated communication identity with the plurality of communication identities in the communication data structure, select, via a selection algorithm, a unit within the plurality of units,

wherein, selecting, via a selection algorithm, a unit within the plurality of units includes:

determining one or more communication identities stored within the communication data structure that may approximately match the generated communication identity,

providing the user with one or more feedbacks associated with one or more approximately matched communication identities,

receiving, from the user, a selected feedback from the one or more feedbacks associated with one or more approximately matched communication identities, and

selecting the unit associated with the selected communication identities,

wherein the selection algorithm is a machine learning algorithm, the machine learning algorithm including a computer-implemented method of selecting a unit within the plurality of units including:

receiving an input dataset comprising a user's historical communication data and/or other users' historical communication data;

determining a user's expected feedback; and

producing an output dataset comprising a unit selection associated with the user's expected feedback;

selecting a unit associated with the matched communication identity within the plurality of identities; and

generate a first feedback in response to the first user input according to the selected communication path.

9. The system of claim 8 , wherein the communication identity is generated by a method of updating the plurality of identity elements via a unit within the plurality of units and one or more user inputs.

10. The system of claim 8 , wherein the plurality of identity elements includes one or more of communication content, user interest, user gender, user occupation, user education, user nationality, user race, user hobbies, user personality, or user demographic.

11. The system of claim 8 , wherein the communication identity is generated via a machine learning algorithm, the machine learning algorithm including a computer-implemented method including:

receiving an input dataset comprising one or more identity elements, the identity elements including one or more of communication content, user interest, user gender, user occupation, user education, user nationality, user race, user hobbies, user personality, user demographic, or user input;

predict a communication identity based on the input dataset; and

producing an output dataset comprising a communication identity.

12. The system of claim 11 , wherein the input dataset includes one or more identity elements based on existing user data.

13. The system of claim 12 , wherein the existing user data includes users' historical voice call logs or chat messages.

14. The system of claim 8 , wherein the communication data structure is constructed via a machine learning algorithm, the machine learning algorithm including a computer-implemented method including:

receiving a training dataset comprising one or more of a group of questions and responses, variations of a group of questions and answers, information specific to an industry, a group of identity elements, a group of communication identities, and/or a communication data structure associated with another chatbot;

storing the training dataset in a communication database; and

producing the communication data structure based on the training dataset.

15. A non-transitory computer-readable storage medium having data stored therein representing software executable by a computer, the software having instructions to:

select a first communication channel from multiple communication channels;

receive, via the first communication channel, a first user input as part of a conversation with a chatbot, the chatbot including a communication data structure including a plurality of communication paths, and a plurality of communication identities,

wherein each of the plurality of communication paths comprises a plurality of units, and

wherein at least one of the plurality of units is coded with instructions to navigate the conversation among different communication paths;

generating a communication identity based on the first user input,

wherein the communication identity includes a plurality of identity elements;

select a communication path based on the first user input, wherein selecting the communication path includes:

comparing the generated communication identity with the plurality of communication identities in the communication data structure to determine a match;

when there is no match between the generated communication identity with the plurality of communication identities in the communication data structure, select, via a selection algorithm, a unit within the plurality of units,

wherein, selecting, via a selection algorithm, a unit within the plurality of units includes:

determining one or more communication identities stored within the communication data structure that may approximately match the generated communication identity,

providing the user with one or more feedbacks associated with one or more approximately matched communication identities,

receiving, from the user, a selected feedback from the one or more feedbacks associated with one or more approximately matched communication identities, and

selecting the unit associated with the selected communication identities,

wherein the selection algorithm is a machine learning algorithm, the machine learning algorithm including a computer-implemented method of selecting a unit within the plurality of units including:

receiving an input dataset comprising a user's historical communication data and/or other users' historical communication data;

determining a user's expected feedback; and

producing an output dataset comprising a unit selection associated with the user's expected feedback;

selecting a unit associated with the matched communication identity within the plurality of identities; and

generate a first feedback in response to the first user input according to the selected communication path.